From a34d6f4f23ecadece068b6a7709706b7b7e8ee0a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=BChle=2C=20Laura=20Christine=20=28lakue103=29?=
<laura.kuehle@uni-duesseldorf.de>
Date: Tue, 15 Feb 2022 17:20:24 +0100
Subject: [PATCH] Added saving of model evaluation data.
---
ANN_Training.py | 11 ++++++++++-
1 file changed, 10 insertions(+), 1 deletion(-)
diff --git a/ANN_Training.py b/ANN_Training.py
index a2ce692..638f94d 100644
--- a/ANN_Training.py
+++ b/ANN_Training.py
@@ -6,11 +6,14 @@ Code-Style: E226, W503
Docstring-Style: D200, D400
TODO: Test new ANN set-up with Soraya
-TODO: Remove object set-up (for more flexibility) -> Done (decided against it to keep easy test set-up)
+TODO: Remove object set-up (for more flexibility) -> Done
+ (decided against it to keep easy test set-up)
TODO: Add documentation
TODO: Allow multiple approximations in one config -> Done
TODO: Split workflow into multiple modules -> Done
TODO: Remove unnecessary instance variables -> Done
+TODO: Add option to change 'num_iterations' for model testing -> Done
+TODO: Save model evaluation data -> Done
TODO: Add README for ANN training
TODO: Fix random seed
TODO: Discuss whether to outsource scripts into separate directory
@@ -24,6 +27,7 @@ import matplotlib
from matplotlib import pyplot as plt
import os
import torch
+import json
from torch.utils.data import TensorDataset, DataLoader, random_split
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_auc_score
@@ -196,6 +200,11 @@ def evaluate_models(models, directory, num_iterations=100, colors=None,
print('Finished training models with 5-fold cross validation!')
print(f'Training time: {toc_train - tic_train:0.4f}s\n')
+ with open(directory + '/' + '_'.join(models.keys()) + '.json', 'w') as json_file:
+ json_file.write(json.dumps(classification_stats))
+ with open(directory + '/' + '_'.join(models.keys()) + '.json') as json_file:
+ classification_stats = json.load(json_file)
+
print('Plotting evaluation of trained models.')
plot_boxplot(classification_stats, colors)
classification_stats = {measure: {model + ' (' + dataset + ')': np.array(
--
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